Bibliographic record
Abstract
Alexithymia is thought of as a trait that predisposes to drug abuse. Moreover, it is suggested to be related to type of the substance abused, with the worst-case scenario including a worse prognosis as well as tendency to relapse or even not to seek treatment at all. To address this important subject in Egyptian patients, a sample of 200 Egyptian substance abusers was randomly selected from inpatients in the Institute of Psychiatry, Ain Shams University, Egypt. The study also included 200 group-matched controls. DSM-IV criteria were used for assessment of substance use disorders, and toxicologic urine analysis was used to confirm the substances of abuse. Toronto Alexithymia Scale (TAS)-Arabic version was used for assessment of alexithymia. It was found that alexithymia was significantly more prevalent in the substance use disorders group as compared to healthy controls. It was also found that among the substance use disorders group, alexithymics reported more polysubstance abuse, more opiate use (other than heroin IV), lower numbers of hospitalizations, lower numbers of reported relapses, and a lower tendency to relapse as a result of internal cues compared to patients without alexithymia. Statistically significant associations were also found between alexithymia and more benzodiazepine abuse and nonpersistence in treatment. The results suggest that alexithymia should be targeted in a treatment setting for substance use disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".